U.S. patent application number 16/875739 was filed with the patent office on 2021-11-18 for dynamic parameter server for autonomous driving vehicles.
The applicant listed for this patent is Baidu USA LLC. Invention is credited to Fan ZHU.
Application Number | 20210356961 16/875739 |
Document ID | / |
Family ID | 1000004870612 |
Filed Date | 2021-11-18 |
United States Patent
Application |
20210356961 |
Kind Code |
A1 |
ZHU; Fan |
November 18, 2021 |
DYNAMIC PARAMETER SERVER FOR AUTONOMOUS DRIVING VEHICLES
Abstract
According to one embodiment, a dynamic parameter server is
provided in an ADV to update parameters of an autonomous driving
system (ADS) of the ADV in real time without requiring the reboot
of the ADS. The dynamic parameter server can obtain new parameters
from a configuration file created by users based on their
experiences and expectations. Each new parameter is mapped to
certain physical conditions. When the ADV encounters the physical
conditions mapped to a particular parameter, the dynamic parameter
server can broadcast the new parameters to the ADS, which can use
the new parameters to control the ADV. The physical conditions can
be used as selection factors for the dynamic parameter to determine
which ADS parameter to update.
Inventors: |
ZHU; Fan; (Sunnyvale,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Baidu USA LLC |
Sunnyvale |
CA |
US |
|
|
Family ID: |
1000004870612 |
Appl. No.: |
16/875739 |
Filed: |
May 15, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/3233 20130101;
G05D 1/0212 20130101; G05D 2201/0213 20130101; G05D 1/0246
20130101; G05D 1/0274 20130101; G05D 1/0088 20130101; G05D 1/0257
20130101; G06T 2207/20104 20130101; G05D 1/0278 20130101 |
International
Class: |
G05D 1/00 20060101
G05D001/00; G05D 1/02 20060101 G05D001/02; G06K 9/32 20060101
G06K009/32 |
Claims
1. A computer-implemented method of dynamically updating parameters
of an autonomous driving system in an autonomous driving vehicle
(ADV), comprising: determining, by a dynamic parameter server in
the ADV, that the ADV is to enter a particular region on a high
definition (HD) map, wherein the ADV includes an autonomous driving
system (ADS) with a plurality of modules, each module having one or
more default parameters; identifying, by the dynamic parameter
server, one or more parameters from a data structure based on one
or more selection factors related to the particular region, each
identified parameter corresponding to a default parameter of one of
the plurality of default modules of the ADS; sending, by the
dynamic parameter server, the one or more identified parameters to
one or more modules of the ADS; and operating, by the ADS, the ADV
using the identified parameters when the ADV drives across the
particular region.
2. The method of claim 1, wherein the one or more selection factors
includes a map identifier of the HD map, a road ID, a lane ID, or a
GPS barrier at the particular region.
3. The method of claim 1, wherein the one or more identified
parameters include one or more of an obstacle to be trimmed from
the region of interest (ROI), a floating obstacle to be trimmed
from the HD map, a speed limit, slope information, or a weight of a
cost function for the ADS.
4. The method of claim 1, wherein the dynamic parameter server
further sends load information of the ADV to the ADS, which uses
the load information to adjust a brake command or a throttle
command of the ADV to maintain a same deceleration or
acceleration.
5. The method of claim 1, wherein the data structure includes a
plurality of entries, each entry representing a matching between
one or more selection factors and a parameter of the ADS.
6. The method of claim 5, wherein the data structure is updated
with information from a configuration file at a predetermined
location in response to the dynamic parameter server being
rebooted.
7. The method of claim 1, wherein the dynamic parameter server is a
separate software module from each autonomous driving module of the
ADS, and communicates with the autonomous driving module via an
internet hub.
8. The method of claim 7, wherein the dynamic parameter server
broadcast each of the one or more identified parameters via the
internet hub to each module in the ADS.
9. A non-transitory machine-readable medium having instructions
stored therein for dynamically updating parameters of an autonomous
driving system in an autonomous driving vehicle (ADV), which
instructions when executed by a processor, cause the processor to
perform operations, the operations comprising: determining, by a
dynamic parameter server in the ADV, that the ADV is to enter a
particular region on a high definition (HD) map, wherein the ADV
includes an autonomous driving system (ADS) with a plurality of
modules, each module having one or more default parameters;
identifying, by the dynamic parameter server, one or more
parameters from a data structure based on one or more selection
factors related to the particular region, each identified parameter
corresponding to a default parameter of one of the plurality of
default modules of the ADS; sending, by the dynamic parameter
server, the one or more identified parameters to one or more
modules of the ADS; and operating, by the ADS, the ADV using the
identified parameters when the ADV drives across the particular
region.
10. The non-transitory machine-readable medium of claim 9, wherein
the one or more selection factors includes a map identifier of the
HD map, a road ID, a lane ID, or a GPS barrier at the particular
region.
11. The non-transitory machine-readable medium of claim 9, wherein
the one or more identified parameters include one or more of an
obstacle to be trimmed from the region of interest (ROI), a
floating obstacle to be trimmed from the HD map, a speed limit,
slope information, or a weight of a cost function for the ADS.
12. The non-transitory machine-readable medium of claim 9, wherein
the dynamic parameter server further sends load information of the
ADV to the ADS, which uses the load information to adjust a brake
command or a throttle command of the ADV to maintain a same
deceleration or acceleration.
13. The non-transitory machine-readable medium of claim 9, wherein
the data structure includes a plurality of entries, each entry
representing a matching between one or more selection factors and a
parameter of the ADS.
14. The non-transitory machine-readable medium of claim 13, wherein
the data structure is updated with information from a configuration
file at a predetermined location in response to the dynamic
parameter server being rebooted.
15. The non-transitory machine-readable medium of claim 9, wherein
the dynamic parameter server is a separate software module from
each autonomous driving module of the ADS, and communicates with
the autonomous driving module via an internet hub.
16. The non-transitory machine-readable medium of claim 15, wherein
the dynamic parameter server broadcast each of the one or more
identified parameters via the internet hub to each module in the
ADS.
17. A data processing system, comprising: a processor; and a memory
coupled to the processor to store instructions for dynamically
updating parameters of an autonomous driving system in an
autonomous driving vehicle (ADV), which instructions when executed
by the processor, cause the processor to perform operations, the
operations including determining, by a dynamic parameter server in
the ADV, that the ADV is to enter a particular region on a high
definition (HD) map, wherein the ADV includes an autonomous driving
system (ADS) with a plurality of modules, each module having one or
more default parameters, identifying, by the dynamic parameter
server, one or more parameters from a data structure based on one
or more selection factors related to the particular region, each
identified parameter corresponding to a default parameter of one of
the plurality of default modules of the ADS, sending, by the
dynamic parameter server, the one or more identified parameters to
one or more modules of the ADS, and operating, by the ADS, the ADV
using the identified parameters when the ADV drives across the
particular region.
18. The system of claim 17, wherein the one or more selection
factors includes a map identifier of the HD map, a road ID, a lane
ID, or a GPS barrier at the particular region.
19. The system of claim 17, wherein the one or more identified
parameters include one or more of an obstacle to be trimmed from
the region of interest (ROI), a floating obstacle to be trimmed
from the HD map, a speed limit, slope information, or a weight of a
cost function for the ADS.
20. The system of claim 17, wherein the dynamic parameter server
further sends load information of the ADV to the ADS, which uses
the load information to adjust a brake command or a throttle
command of the ADV to maintain a same deceleration or acceleration.
Description
TECHNICAL FIELD
[0001] Embodiments of the present disclosure relate generally to
operating autonomous driving vehicles. More particularly,
embodiments of the disclosure relate to a method of dynamically
overwriting some default parameters of an autonomous driving system
of an autonomous driving vehicle.
BACKGROUND
[0002] Vehicles operating in an autonomous mode (e.g., driverless)
can relieve occupants, especially the driver, from some
driving-related responsibilities. When operating in an autonomous
mode, the vehicle can navigate to various locations using onboard
sensors, allowing the vehicle to travel with minimal human
interaction or in some cases without any passengers.
[0003] An autonomous driving vehicle (ADV) can use hardware sensors
to perceive the driving environment, and use sensor data coupled
with a high definition (HD) map for path planning. An ADV may also
include a number of software modules to process sensor data and map
information, generate paths, and control the operation of the
ADV.
[0004] Each software module can have a set of default parameters.
However, these default parameters are not optimal for all HD maps,
or different regions within the same HD map. Although default
parameters of each software module can be directly modified
manually by a user, such direct modification is not only
time-consuming but also requires a reboot of the software modules,
which may interrupt the normal operation of the ADV.
[0005] Default parameters of each software module may also be
modified programmatically by the ADV in real-time based on driving
conditions of the ADV. This approach is also problematic. One of
the disadvantages for this approach is that the programmatically
modified parameters may not always ideal for an HD map. Another
disadvantage is that sometimes the ADV cannot detect certain
physical conditions (e.g., a gentle slope on the road), and the
slope information may not be available by the HD map either. As a
result, the ADV may not be able to use such information to
programmatically modify certain default parameters.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] Embodiments of the disclosure are illustrated by way of
example and not limitation in the figures of the accompanying
drawings in which like references indicate similar elements.
[0007] FIG. 1 is a block diagram illustrating a networked system
according to one embodiment.
[0008] FIG. 2 is a block diagram illustrating an example of an
autonomous driving vehicle according to one embodiment.
[0009] FIGS. 3A-3B are block diagrams illustrating an example of an
autonomous driving system used with an autonomous driving vehicle
according to one embodiment.
[0010] FIG. 4 is a block diagram illustrating an example of a
system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0011] FIG. 5 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0012] FIG. 6 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an autonomous
driving system in an ADV according to one embodiment.
[0013] FIG. 7 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0014] FIG. 8 is a flow diagram illustrating a process of
dynamically updating parameters of an ADS in an ADV according to
one embodiment.
[0015] FIG. 9 is a flow diagram illustrating another process of
dynamically updating parameters of an ADS in an ADV according to
one embodiment.
DETAILED DESCRIPTION
[0016] Various embodiments and aspects of the disclosures will be
described with reference to details discussed below, and the
accompanying drawings will illustrate the various embodiments. The
following description and drawings are illustrative of the
disclosure and are not to be construed as limiting the disclosure.
Numerous specific details are described to provide a thorough
understanding of various embodiments of the present disclosure.
However, in certain instances, well-known or conventional details
are not described in order to provide a concise discussion of
embodiments of the present disclosures.
[0017] Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in conjunction with the embodiment can be
included in at least one embodiment of the disclosure. The
appearances of the phrase "in one embodiment" in various places in
the specification do not necessarily all refer to the same
embodiment.
[0018] According to some embodiments, a dynamic parameter server is
provided in an ADV to update parameters of an autonomous driving
system (ADS) of the ADV in real time without requiring the reboot
of the ADS. The dynamic parameter server can obtain new parameters
from a configuration file created by users based on their
experiences and expectations. Each new parameter is mapped to
certain physical conditions. When the ADV encounters the physical
conditions mapped to a particular parameter, the dynamic parameter
server can broadcast the new parameters to the ADS, which can use
the new parameters to control the ADV. The physical conditions can
be used as selection factors for the dynamic parameter to determine
which ADS parameter to update.
[0019] According to an exemplary method, before the ADV is to enter
a particular region on a high definition (HD) map, the dynamic
parameter server identifies one or more parameters from a data
structure based on one or more selection factors related to the
particular region, each identified parameter corresponding to a
parameter of an autonomous driving module in the ADS. The dynamic
parameter server broadcasts the one or more identified parameters
via an internet hub to each autonomous driving module in the ADS,
which can update one or more corresponding default parameters of
the ADS with the one or more identified parameters. Thereafter,
when driving across the particular region, the ADS uses the updated
parameters to operate the ADV until the parameters gets updated
again.
[0020] In one embodiment, the selection factors can include one or
more of a map ID, a road ID, a lane ID, and a GPS barrier. A GPS
barrier as used herein refers to a region determined by GPS
absolute coordinates; it typically is a rectangle but can be any
shape.
[0021] In one embodiment, the one or more identified parameters can
be values predetermined by users. A user can create a configuration
file with the desired parameters and place the configuration file
at a particular folder or director in a computing device of the
ADV. From the particular folder or directory, the dynamic parameter
server can pick up the configuration file and load it into a
searchable data structure, such as a table. The identified
parameters can include one or more of an obstacle to be trimmed
from a region of interest (ROI), a floating obstacle to be trimmed
from the HD map, a speed limit, slope information, or a weight of a
cost function for the planning module of the ADS.
[0022] In one embodiment, the dynamic parameter server can
additionally identify one or more parameters that is not related to
any geographic region for updating corresponding default parameters
of the ADS. For example, the dynamic parameter server can receive
information about the load of the ADV, and broadcast the load
information to the ADS, which uses the load information as an input
to a control module if the ADS. The control module can use the load
information to adjust brake commands and/or throttle commands to
maintain a constant acceleration/deceleration for the ADV.
[0023] In one embodiment, the data structure can be a table, and
can include entries of mapping between one or more selection
factors and a parameter of the ADS. The data structure can be
updated with information from a configuration file at a
predetermined location in response to the dynamic parameter server
being rebooted.
[0024] In one embodiment, the dynamic parameter server is a
separate software module from each autonomous driving module of the
ADS, and communicates with the autonomous driving module via an
internet hub. The dynamic parameter server broadcast each of the
one or more identified parameters via the internet hub to each
module in the ADS.
[0025] The above summary does not include an exhaustive list of all
embodiments in this disclosure. All methods described above can be
practiced from all suitable combinations of the various aspects and
embodiments described in the disclosure.
Autonomous Driving Vehicle
[0026] FIG. 1 is a block diagram illustrating an autonomous driving
network configuration according to one embodiment of the
disclosure. Referring to FIG. 1, network configuration 100 includes
autonomous driving vehicle (ADV) 101 that may be communicatively
coupled to one or more servers 103-104 over a network 102. Although
there is one ADV shown, multiple ADVs can be coupled to each other
and/or coupled to servers 103-104 over network 102. Network 102 may
be any type of networks such as a local area network (LAN), a wide
area network (WAN) such as the Internet, a cellular network, a
satellite network, or a combination thereof, wired or wireless.
Server(s) 103-104 may be any kind of servers or a cluster of
servers, such as Web or cloud servers, application servers, backend
servers, or a combination thereof. Servers 103-104 may be data
analytics servers, content servers, traffic information servers,
map and point of interest (MPOI) servers, or location servers,
etc.
[0027] An ADV refers to a vehicle that can be configured to in an
autonomous mode in which the vehicle navigates through an
environment with little or no input from a driver. Such an ADV can
include a sensor system having one or more sensors that are
configured to detect information about the environment in which the
vehicle operates. The vehicle and its associated controller(s) use
the detected information to navigate through the environment. ADV
101 can operate in a manual mode, a full autonomous mode, or a
partial autonomous mode.
[0028] In one embodiment, ADV 101 includes, but is not limited to,
autonomous driving system (ADS) 110, vehicle control system 111,
wireless communication system 112, user interface system 113, and
sensor system 115. ADV 101 may further include certain common
components included in ordinary vehicles, such as, an engine,
wheels, steering wheel, transmission, etc., which may be controlled
by vehicle control system 111 and/or ADS 110 using a variety of
communication signals and/or commands, such as, for example,
acceleration signals or commands, deceleration signals or commands,
steering signals or commands, braking signals or commands, etc.
[0029] Components 110-115 may be communicatively coupled to each
other via an interconnect, a bus, a network, or a combination
thereof. For example, components 110-115 may be communicatively
coupled to each other via a controller area network (CAN) bus. A
CAN bus is a vehicle bus standard designed to allow
microcontrollers and devices to communicate with each other in
applications without a host computer. It is a message-based
protocol, designed originally for multiplex electrical wiring
within automobiles, but is also used in many other contexts.
[0030] Referring now to FIG. 2, in one embodiment, sensor system
115 includes, but it is not limited to, one or more cameras 211,
global positioning system (GPS) unit 212, inertial measurement unit
(IMU) 213, radar unit 214, and a light detection and range (LIDAR)
unit 215. GPS system 212 may include a transceiver operable to
provide information regarding the position of the ADV. IMU unit 213
may sense position and orientation changes of the ADV based on
inertial acceleration. Radar unit 214 may represent a system that
utilizes radio signals to sense objects within the local
environment of the ADV. In some embodiments, in addition to sensing
objects, radar unit 214 may additionally sense the speed and/or
heading of the objects. LIDAR unit 215 may sense objects in the
environment in which the ADV is located using lasers. LIDAR unit
215 could include one or more laser sources, a laser scanner, and
one or more detectors, among other system components. Cameras 211
may include one or more devices to capture images of the
environment surrounding the ADV. Cameras 211 may be still cameras
and/or video cameras. A camera may be mechanically movable, for
example, by mounting the camera on a rotating and/or tilting a
platform.
[0031] Sensor system 115 may further include other sensors, such
as, a sonar sensor, an infrared sensor, a steering sensor, a
throttle sensor, a braking sensor, and an audio sensor (e.g.,
microphone). An audio sensor may be configured to capture sound
from the environment surrounding the ADV. A steering sensor may be
configured to sense the steering angle of a steering wheel, wheels
of the vehicle, or a combination thereof. A throttle sensor and a
braking sensor sense the throttle position and braking position of
the vehicle, respectively. In some situations, a throttle sensor
and a braking sensor may be integrated as an integrated
throttle/braking sensor.
[0032] In one embodiment, vehicle control system 111 includes, but
is not limited to, steering unit 201, throttle unit 202 (also
referred to as an acceleration unit), and braking unit 203.
Steering unit 201 is to adjust the direction or heading of the
vehicle. Throttle unit 202 is to control the speed of the motor or
engine that in turn controls the speed and acceleration of the
vehicle. Braking unit 203 is to decelerate the vehicle by providing
friction to slow the wheels or tires of the vehicle. Note that the
components as shown in FIG. 2 may be implemented in hardware,
software, or a combination thereof.
[0033] Referring back to FIG. 1, wireless communication system 112
is to allow communication between ADV 101 and external systems,
such as devices, sensors, other vehicles, etc. For example,
wireless communication system 112 can wirelessly communicate with
one or more devices directly or via a communication network, such
as servers 103-104 over network 102. Wireless communication system
112 can use any cellular communication network or a wireless local
area network (WLAN), e.g., using WiFi to communicate with another
component or system. Wireless communication system 112 could
communicate directly with a device (e.g., a mobile device of a
passenger, a display device, a speaker within vehicle 101), for
example, using an infrared link, Bluetooth, etc. User interface
system 113 may be part of peripheral devices implemented within
vehicle 101 including, for example, a keyboard, a touch screen
display device, a microphone, and a speaker, etc.
[0034] Some or all of the functions of ADV 101 may be controlled or
managed by ADS 110, especially when operating in an autonomous
driving mode. ADS 110 includes the necessary hardware (e.g.,
processor(s), memory, storage) and software (e.g., operating
system, planning and routing programs) to receive information from
sensor system 115, control system 111, wireless communication
system 112, and/or user interface system 113, process the received
information, plan a route or path from a starting point to a
destination point, and then drive vehicle 101 based on the planning
and control information. Alternatively, ADS 110 may be integrated
with vehicle control system 111.
[0035] For example, a user as a passenger may specify a starting
location and a destination of a trip, for example, via a user
interface. ADS 110 obtains the trip related data. For example, ADS
110 may obtain location and route data from an MPOI server, which
may be a part of servers 103-104. The location server provides
location services and the MPOI server provides map services and the
POIs of certain locations. Alternatively, such location and MPOI
information may be cached locally in a persistent storage device of
ADS 110.
[0036] While ADV 101 is moving along the route, ADS 110 may also
obtain real-time traffic information from a traffic information
system or server (TIS). Note that servers 103-104 may be operated
by a third party entity. Alternatively, the functionalities of
servers 103-104 may be integrated with ADS 110. Based on the
real-time traffic information, MPOI information, and location
information, as well as real-time local environment data detected
or sensed by sensor system 115 (e.g., obstacles, objects, nearby
vehicles), ADS 110 can plan an optimal route and drive vehicle 101,
for example, via control system 111, according to the planned route
to reach the specified destination safely and efficiently.
[0037] Server 103 may be a data analytics system to perform data
analytics services for a variety of clients. In one embodiment,
data analytics system 103 includes data collector 121 and machine
learning engine 122. Data collector 121 collects driving statistics
123 from a variety of vehicles, either ADVs or regular vehicles
driven by human drivers. Driving statistics 123 include information
indicating the driving commands (e.g., throttle, brake, steering
commands) issued and responses of the vehicles (e.g., speeds,
accelerations, decelerations, directions) captured by sensors of
the vehicles at different points in time. Driving statistics 123
may further include information describing the driving environments
at different points in time, such as, for example, routes
(including starting and destination locations), MPOIs, road
conditions, weather conditions, etc.
[0038] Based on driving statistics 123, machine learning engine 122
generates or trains a set of rules, algorithms, and/or predictive
models 124 for a variety of purposes. Algorithms 124 can then be
uploaded on ADVs to be utilized during autonomous driving in
real-time.
[0039] FIGS. 3A and 3B are block diagrams illustrating an example
of an autonomous driving system used with an ADV according to one
embodiment. System 300 may be implemented as a part of ADV 101 of
FIG. 1 including, but is not limited to, ADS 110, control system
111, and sensor system 115. Referring to FIGS. 3A-3B, ADS 110
includes, but is not limited to, localization module 301,
perception module 302, prediction module 303, decision module 304,
planning module 305, control module 306, routing module 307, and
dynamic parameter sever 308.
[0040] Some or all of modules 301-308 may be implemented in
software, hardware, or a combination thereof. For example, these
modules may be installed in persistent storage device 352, loaded
into memory 351, and executed by one or more processors (not
shown). Note that some or all of these modules may be
communicatively coupled to or integrated with some or all modules
of vehicle control system 111 of FIG. 2. Some of modules 301-308
may be integrated together as an integrated module.
[0041] Localization module 301 determines a current location of ADV
300 (e.g., leveraging GPS unit 212) and manages any data related to
a trip or route of a user. Localization module 301 (also referred
to as a map and route module) manages any data related to a trip or
route of a user. A user may log in and specify a starting location
and a destination of a trip, for example, via a user interface.
Localization module 301 communicates with other components of ADV
300, such as map and route data 311, to obtain the trip related
data. For example, localization module 301 may obtain location and
route data from a location server and a map and POI (MPOI) server.
A location server provides location services and an MPOI server
provides map services and the POIs of certain locations, which may
be cached as part of map and route data 311. While ADV 300 is
moving along the route, localization module 301 may also obtain
real-time traffic information from a traffic information system or
server.
[0042] Based on the sensor data provided by sensor system 115 and
localization information obtained by localization module 301, a
perception of the surrounding environment is determined by
perception module 302. The perception information may represent
what an ordinary driver would perceive surrounding a vehicle in
which the driver is driving. The perception can include the lane
configuration, traffic light signals, a relative position of
another vehicle, a pedestrian, a building, crosswalk, or other
traffic related signs (e.g., stop signs, yield signs), etc., for
example, in a form of an object. The lane configuration includes
information describing a lane or lanes, such as, for example, a
shape of the lane (e.g., straight or curvature), a width of the
lane, how many lanes in a road, one-way or two-way lane, merging or
splitting lanes, exiting lane, etc.
[0043] Perception module 302 may include a computer vision system
or functionalities of a computer vision system to process and
analyze images captured by one or more cameras in order to identify
objects and/or features in the environment of the ADV. The objects
can include traffic signals, road way boundaries, other vehicles,
pedestrians, and/or obstacles, etc. The computer vision system may
use an object recognition algorithm, video tracking, and other
computer vision techniques. In some embodiments, the computer
vision system can map an environment, track objects, and estimate
the speed of objects, etc. Perception module 302 can also detect
objects based on other sensors data provided by other sensors such
as a radar and/or LIDAR.
[0044] For each of the objects, prediction module 303 predicts what
the object will behave under the circumstances. The prediction is
performed based on the perception data perceiving the driving
environment at the point in time in view of a set of map and route
data 311 and traffic rules 312. For example, if the object is a
vehicle at an opposing direction and the current driving
environment includes an intersection, prediction module 303 will
predict whether the vehicle will likely move straight forward or
make a turn. If the perception data indicates that the intersection
has no traffic light, prediction module 303 may predict that the
vehicle may have to fully stop prior to enter the intersection. If
the perception data indicates that the vehicle is currently at a
left-turn only lane or a right-turn only lane, prediction module
303 may predict that the vehicle will more likely make a left turn
or right turn respectively.
[0045] For each of the objects, decision module 304 makes a
decision regarding how to handle the object. For example, for a
particular object (e.g., another vehicle in a crossing route) as
well as its metadata describing the object (e.g., a speed,
direction, turning angle), decision module 304 decides how to
encounter the object (e.g., overtake, yield, stop, pass). Decision
module 304 may make such decisions according to a set of rules such
as traffic rules or driving rules 312, which may be stored in
persistent storage device 352.
[0046] Routing module 307 is configured to provide one or more
routes or paths from a starting point to a destination point. For a
given trip from a start location to a destination location, for
example, received from a user, routing module 307 obtains map and
route data 311 and determines all possible routes or paths from the
starting location to reach the destination location. Routing module
307 may generate a reference line in a form of a topographic map
for each of the routes it determines from the starting location to
reach the destination location. A reference line refers to an ideal
route or path without any interference from others such as other
vehicles, obstacles, or traffic condition. That is, if there is no
other vehicle, pedestrians, or obstacles on the road, an ADV should
exactly or closely follows the reference line. The topographic maps
are then provided to decision module 304 and/or planning module
305. Decision module 304 and/or planning module 305 examine all of
the possible routes to select and modify one of the most optimal
routes in view of other data provided by other modules such as
traffic conditions from localization module 301, driving
environment perceived by perception module 302, and traffic
condition predicted by prediction module 303. The actual path or
route for controlling the ADV may be close to or different from the
reference line provided by routing module 307 dependent upon the
specific driving environment at the point in time.
[0047] Based on a decision for each of the objects perceived,
planning module 305 plans a path or route for the ADV, as well as
driving parameters (e.g., distance, speed, and/or turning angle),
using a reference line provided by routing module 307 as a basis.
That is, for a given object, decision module 304 decides what to do
with the object, while planning module 305 determines how to do it.
For example, for a given object, decision module 304 may decide to
pass the object, while planning module 305 may determine whether to
pass on the left side or right side of the object. Planning and
control data is generated by planning module 305 including
information describing how vehicle 300 would move in a next moving
cycle (e.g., next route/path segment). For example, the planning
and control data may instruct vehicle 300 to move 10 meters at a
speed of 30 miles per hour (mph), then change to a right lane at
the speed of 25 mph.
[0048] Based on the planning and control data, control module 306
controls and drives the ADV, by sending proper commands or signals
to vehicle control system 111 control system 111 via a CAN bus 321,
according to a route or path defined by the planning and control
data. The planning and control data include sufficient information
to drive the vehicle from a first point to a second point of a
route or path using appropriate vehicle settings or driving
parameters (e.g., throttle, braking, steering commands) at
different points in time along the path or route.
[0049] In one embodiment, the planning phase is performed in a
number of planning cycles, also referred to as driving cycles, such
as, for example, in every time interval of 100 milliseconds (ms).
For each of the planning cycles or driving cycles, one or more
control commands will be issued based on the planning and control
data. That is, for every 100 ms, planning module 305 plans a next
route segment or path segment, for example, including a target
position and the time required for the ADV to reach the target
position. Alternatively, planning module 305 may further specify
the specific speed, direction, and/or steering angle, etc. In one
embodiment, planning module 305 plans a route segment or path
segment for the next predetermined period of time such as 5
seconds. For each planning cycle, planning module 305 plans a
target position for the current cycle (e.g., next 5 seconds) based
on a target position planned in a previous cycle. Control module
306 then generates one or more control commands (e.g., throttle,
brake, steering control commands) based on the planning and control
data of the current cycle.
[0050] Note that decision module 304 and planning module 305 may be
integrated as an integrated module. Decision module 304/planning
module 305 may include a navigation system or functionalities of a
navigation system to determine a driving path for the ADV. For
example, the navigation system may determine a series of speeds and
directional headings to affect movement of the ADV along a path
that substantially avoids perceived obstacles while generally
advancing the ADV along a roadway-based path leading to an ultimate
destination. The destination may be set according to user inputs
via user interface system 113. The navigation system may update the
driving path dynamically while the ADV is in operation. The
navigation system can incorporate data from a GPS system and one or
more maps so as to determine the driving path for the ADV.
[0051] The dynamic parameter server 308 can be a software module in
the ADS 110 that can be used to update parameters of the ADS 110
without requiring the reboot of the ADS. The dynamic parameter
server can obtain new parameters from a configuration file created
by users based on their experiences and expectations. When the ADV
101 operating in an autonomous driving mode encounters physical
conditions mapped to a particular parameter, the dynamic parameter
server can broadcast the new parameters to the ADS, which can use
the new parameters to control the ADV.
Dynamic Parameter Server
[0052] FIG. 4 is a block diagram illustrating an example of a
system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0053] As shown in FIG. 4, a dynamic parameter server 308 can be
provided as a separate entity from each autonomous driving module
in the ADS. The dynamic parameter server 308 can take input from
the localization module 301, the map and route data component 311,
and the Can bus component 321.
[0054] From the map and route data component 311, the dynamic
parameter server can determine an ID of a lane 413 that the ADV 101
is currently taking, an ID of a road 411 that includes the lane,
and an ID of a HD map 407 corresponding to the area on which the
ADV 101 is travelling. From the localization module 301, the
dynamic parameter server 308 can take a GPS barrier 409.
[0055] The input to the dynamic parameter server 308 from the
localization module 301 and the map and route data component 311
can be used as selection factors to select new ADS parameters to
update default parameters of the ADS 110. Some of the selection
factors are related to a particular geographic region on a HD map,
and are used to indicate a location of a vehicle on the HD map, for
example, the lane ID, while some others are related to sensor data,
for example, a GPS barrier 409, which is a boundary formed by
absolute GPS coordinates on a HD map.
[0056] In one embodiment, the dynamic parameter server 308 includes
a data structure 417 loaded with mappings between ADS parameters
and selection factors 405. The data structure 417 can be any
searchable entity, for example, a table. The searchable data
structure 417 can be populated with information from a parameters
configuration document 401, which can be edited by a user manually
or via a user interface or via a tool. Examples of the parameters
configuration document is a JavaScript Object Notion (JSON) file,
or a Hypertext Markup Language (HTML) file.
[0057] In one embodiment, data in the data structure 417 can be
structured in a way that allows the dynamic parameter server 308 to
obtain a different set of ADS parameters for each unique
combination of the selection factors 405.
[0058] For example, when the ADV 101 is traveling on an HD map A,
the dynamic parameter server 308 can obtain the ID of HD map A from
the map and route data 311, and use the map ID to obtain a set of
ADS parameters for the map from the data structure 417. As the ADV
101 enters road A on HD map A, a different set of ADS parameters
for the road can be obtained from the data structure 417.
Similarly, as the ADV 101 enters lane A in road A, another set of
ADS parameters can be obtained from the data structure 417.
Further, depending on whether the ADV 101 is within a GPS barrier
or not, the dynamic parameter server 308 can have a different set
of optimal parameters for the ADS.
[0059] In one embodiment, once a set of ADS parameters is
identified from the data structure 417 based on the location of the
ADV 101, the dynamic parameter server 308 can broadcast the ADS
parameters via a network. Each of a number of autonomous driving
modules 302-307 can receive the set of ADS parameters broadcast via
the network. If an AD has a corresponding ADS parameter, the
default value of the corresponding ADS parameter can be updated
with the received value. Thereafter and before the ADS parameter is
updated again with a new value received from the dynamic parameter
server 308, the ADS 110 would use the updated parameter to operate
the ADV 101.
[0060] FIG. 5 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0061] Specifically, this figure illustrates that the dynamic
parameter server 308 identifies a set of parameters for updating
default parameters of the perception module 302. As shown in FIG.
5, the perception module 302 can include a number of parameters.
Although the figure only shows three parameters, default parameter
A 501, default parameter B 503, and default parameter N 505, the
perception module 302 may include one or more other default
parameters.
[0062] In one embodiment, default parameter A 501 can represent an
obstacle to be trimmed from a region of interest (ROI) for the ADV
101. An ROI is a perception area for the ADV 101, and obstacles
outside the ROI tends to be ignored by the ADV 101. An ROI for the
perception module 302 can be saved in the map and route data
component 311, and can be constantly regenerated as the ADV 101 is
travelling in an area corresponding to an HD map.
[0063] Dynamic parameter A 507 identified by the dynamic parameter
server 308 can be an obstacle (e.g., a trash bin on the roadside)
on the HD map that is known by a user to be within the ROI of the
ADV 101. The user may know this from his past experiences or from
the HD map.
[0064] While the ADV 101 in motion, the perception module 302, upon
receiving information (e.g., location information such as map ID,
road ID, etc.) for the trash bin, can exclude the trash bin from
the ROI of the existing map without regenerating the part of the
map corresponding to the area that includes the obstacle.
[0065] Default parameter A 501 may be frequently updated with
different obstacles on the right side of the ADV as opposed to the
left side, because the road curb on the right side tends to have
more obstacles. Accordingly, many obstacles on the left side would
be trimmed from the ROI of the ADV 101.
[0066] In one embodiment, default parameter B 503 can be a floating
obstacle that needs to be trimmed from a particular region on an HD
map. One example of a floating obstacle is a bunch of hanging
willow branches. If a region is known to have hanging willow
branches based on the HD map, the region can be marked by a GPS
barrier or a lane ID in the parameters configuration document 401,
which can be and loaded into the data structure 417.
[0067] The dynamic parameter server 308, upon detecting that the
ADV 101 is about to enter the marked region, can identify the
marked region based on data from the localization module 301 and/or
the map and route data component 311, and broadcast dynamic
parameter B 509 as represented by the identifying information for
the hanging willow branches to the perception module 302. The
perception module 302 can would ignore the hanging willow branches,
for example, by filtering out the hanging willow branches from the
ROI.
[0068] In one embodiment, the perception module 302 may include one
or more default parameters, e.g., default parameter N 505, that are
not to be updated by the dynamic parameter server 308. Further, an
updated parameter of any AD module in the ADS 110 can be used by
the ADS 110 for as long as the physical conditions related to the
dynamic parameter exist. Once the ADV 101 passes the physical
conditions, the ADS 110 would revert the updated parameter back to
the default value. By default, the perception module 302 would not
trim any obstacle from the ROI, or ignore any floating obstacle
from a particular region on the HD map.
[0069] FIG. 6 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an autonomous
driving system in an ADV according to one embodiment. Specifically,
this figure illustrates that the dynamic parameter server 308
identifies a set of parameters for updating default parameters of
the planning module 305.
[0070] As shown in FIG. 6, the planning module 305 can include a
number of default parameters. Although the figure shows three
default parameters, default parameter A 601, default parameter B
603, and default parameter N 605, the planning module 305 may
include one or more additional default parameters.
[0071] Default parameter A 601 can represent a posted speed limit
on a roadside. However, driving at a posted speed limit may not
always ideal. For example, on a road with a posted speed limit of
35 mph, if human driving experiences shows that the traffic is
light in the evening and congested in the daytime, the ADV 101 may
adjust its speed limit to drive faster than the posted speed limit
in the evening, and drive slower than the posted speed limit in the
daytime. The adjusted speed limit as represented by dynamic
parameter A 607 can be broadcast by the dynamic parameter server
308 to the planning module 305.
[0072] Default parameter B 603 can be a weight of a cost function.
The planning module 305 may select the optimal path from a number
of passible paths from point A to point using a cost function,
which may encode traffic rules and road conditions. A cost function
may have different weight parameters that can be adjusted based on
experiences and/or driving statistics. The weight of a particular
factor of the cost function may be different for one region of a
map from the weight of the factor for another region of the map.
Based on where the ADV 101 is driving, one or more weight factors
of a cost function used by the planning module 305 may be adjusted
by a user based on experiences.
[0073] When the ADV 101 enters a particular region, the dynamic
parameter server 308 may identify a weight (represented by dynamic
parameter B 609) of a particular factor of the cost function, and
broadcast the weight to the network, for use by the planning module
305.
[0074] FIG. 7 is a block diagram further illustrating an example of
a system for dynamically updating parameters of an ADS in an ADV
according to one embodiment.
[0075] Specifically, this figure illustrates that the dynamic
parameter server 308 identifies a set of parameters for updating
one or more default parameters of the control module 306.
[0076] As shown in FIG. 7, the control module 306 can include a
number of default parameters. Although the figure shows three
parameters, default parameter A 701, default parameter B 703, and
default parameter N 705, the control module 306 may include one or
more other default parameters.
[0077] In one embodiment, default parameter A 701 may represent
slope information of a particular region on an HD map. The slope
information typically is not included in an HD map, and also is not
easily detected by sensors, particularly when the slope is not that
steep. A user, on the other hand, would be able to tell the
presence of such a slope from his experiences in driving across the
slope.
[0078] Identifiers of the slope (e.g., a map ID, a road ID, and/or
a GPS barrier) may be associated with the region where the slope is
located. The association may be put in the parameters configuration
file 401. When the ADV 101 is to get on the slope, the slope
information as represented by dynamic parameter A 707 may be
obtained by the dynamic parameter server 308 and broadcast to the
control module 306, which may use the slope information to adjust a
throttle and/or brake command to compensate for the gravity changes
associated with the slope.
[0079] In one embodiment, default parameter B 704 may be vehicle
load information that may has a default value being the weight of
the AVD 101. When the load of the ADV 101 changes, for example, due
to additional passenger in the vehicle, a weight sensor in the ADV
101 can sense the weight changes, and send the weight to the CAN
bus 321. The dynamic parameter server 308 can obtain the load
information from the CAN bus 321, and broadcast the information
represented by dynamic parameter B 709 to the control module 306.
The control module 306 can use the load information to adjust a
brake command or a throttle command of the ADV to maintain a same
deceleration or acceleration.
[0080] Unlike the other dynamic parameters described in the
disclosure, the vehicle load information is not specified by a user
based on his experiences. Instead, the load information is
dynamically generated by a weight sensor in real time.
[0081] FIG. 8 is a flow diagram illustrating a process 800 of
dynamically updating parameters of an ADS in an ADV according to
one embodiment. Process 800 may be performed by processing logic
which may include software, hardware, or a combination thereof.
[0082] Referring to FIG. 8, in operation 801, a user creates a
parameters configuration file based on his experiences, and places
the file in a designated folder in a computing device in an
autonomous driving vehicle (ADV), which includes a dynamic
parameter server. In operation 803, the dynamic parameter server,
when rebooted, loads information in the parameters configuration
file to a searchable data structure in the dynamic parameter
server.
[0083] In operation 805, the dynamic parameter server obtains, from
a localization module and a map and a route data component, one or
more of a map ID, a road ID, a lane ID, or a GPS barrier of a
particular region as the ADV is travelling on a road segment. In
operation 807, the dynamic parameter server uses the information
from the localization module and a map and route data component to
identify one or more dynamic parameters associated with the
particular region on the road segment.
[0084] In operation 809, the dynamic parameter server broadcasts
the one or more identified dynamic parameters via a network to
autonomous driving modules in the ADV. In operation 811, one or
more autonomous driving modules in the ADS updates a corresponding
default parameter with each of the one or more identified dynamic
parameters. In operation 813, the ADS uses the updated parameters
to operate the ADV when driving across the particular region on the
road segment.
[0085] FIG. 9 is a flow diagram illustrating a process 900 of
dynamically updating parameters of an ADS in an ADV according to
one embodiment. Process 900 may be performed by processing logic
which may include software, hardware, or a combination thereof. For
example, process 900 may be performed by the dynamic parameter
server 308 described in FIGS. 4 -7 or the ADS 110 as described in
FIGS. 3A-3B.
[0086] Referring to FIG. 9, in operation 901, the processing logic
determines that the ADV is to enter a particular region on a high
definition (HD) map. The ADV includes an autonomous driving system
(ADS) with a plurality of modules, each module having one or more
default parameters. In operation 902, the processing logic
identifies one or more parameters from a data structure based on
one or more selection factors related to the particular region,
each identified parameter corresponding to a default parameter of
one of the plurality of default parameters of the ADS. In operation
903, the processing logic sends the one or more identified
parameters to one or more modules of the ADS. In operation 904, the
processing logic operates the ADV using the identified parameters
when the ADV drives across the particular region.
[0087] Note that some or all of the components as shown and
described above may be implemented in software, hardware, or a
combination thereof. For example, such components can be
implemented as software installed and stored in a persistent
storage device, which can be loaded and executed in a memory by a
processor (not shown) to carry out the processes or operations
described throughout this application. Alternatively, such
components can be implemented as executable code programmed or
embedded into dedicated hardware such as an integrated circuit
(e.g., an application specific IC or ASIC), a digital signal
processor (DSP), or a field programmable gate array (FPGA), which
can be accessed via a corresponding driver and/or operating system
from an application. Furthermore, such components can be
implemented as specific hardware logic in a processor or processor
core as part of an instruction set accessible by a software
component via one or more specific instructions.
[0088] Some portions of the preceding detailed descriptions have
been presented in terms of algorithms and symbolic representations
of operations on data bits within a computer memory. These
algorithmic descriptions and representations are the ways used by
those skilled in the data processing arts to most effectively
convey the substance of their work to others skilled in the art. An
algorithm is here, and generally, conceived to be a self-consistent
sequence of operations leading to a desired result. The operations
are those requiring physical manipulations of physical
quantities.
[0089] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the above discussion, it is appreciated that throughout the
description, discussions utilizing terms such as those set forth in
the claims below, refer to the action and processes of a computer
system, or similar electronic computing device, that manipulates
and transforms data represented as physical (electronic) quantities
within the computer system's registers and memories into other data
similarly represented as physical quantities within the computer
system memories or registers or other such information storage,
transmission or display devices.
[0090] Embodiments of the disclosure also relate to an apparatus
for performing the operations herein. Such a computer program is
stored in a non-transitory computer readable medium. A
machine-readable medium includes any mechanism for storing
information in a form readable by a machine (e.g., a computer). For
example, a machine-readable (e.g., computer-readable) medium
includes a machine (e.g., a computer) readable storage medium
(e.g., read only memory ("ROM"), random access memory ("RAM"),
magnetic disk storage media, optical storage media, flash memory
devices).
[0091] The processes or methods depicted in the preceding figures
may be performed by processing logic that comprises hardware (e.g.
circuitry, dedicated logic, etc.), software (e.g., embodied on a
non-transitory computer readable medium), or a combination of both.
Although the processes or methods are described above in terms of
some sequential operations, it should be appreciated that some of
the operations described may be performed in a different order.
Moreover, some operations may be performed in parallel rather than
sequentially.
[0092] Embodiments of the present disclosure are not described with
reference to any particular programming language. It will be
appreciated that a variety of programming languages may be used to
implement the teachings of embodiments of the disclosure as
described herein.
[0093] In the foregoing specification, embodiments of the
disclosure have been described with reference to specific exemplary
embodiments thereof. It will be evident that various modifications
may be made thereto without departing from the broader spirit and
scope of the disclosure as set forth in the following claims. The
specification and drawings are, accordingly, to be regarded in an
illustrative sense rather than a restrictive sense.
* * * * *